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Command R+ vs Llama 3.3 70B Instruct

How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for Command R+ and Llama 3.3 70B Instruct.

Cohere

Command R+

Cohere's enterprise-optimized model built for advanced Retrieval-Augmented Generation (RAG) and multi-step tool use. Highly effective for multilingual business processes.

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Meta

Llama 3.3 70B Instruct

Meta's state-of-the-art open weights model, providing enterprise-grade reasoning and logic. Exceptionally powerful for self-hosted customer support, text generation, and tooling workflows.

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Technical Specifications

SpecificationCommand R+Llama 3.3 70B Instruct
ProviderCohereMeta
Context Window128,000 tokens131,072 tokens
Agent Suitability86/10083/100
Time to First Token (TTFT)350 ms280 ms
Deployment Modelmanaged apiself hostable
Production Stabilitystablestable
API AvailableYesYes
Released Date2024-04-042024-12-06

API Pricing Comparison

Input Price per Million Tokens

Command R+

$2.50

Llama 3.3 70B Instruct

$0.10

Output Price per Million Tokens

Command R+

$10.00

Llama 3.3 70B Instruct

$0.32

Want to test both models live?

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Benchmark Performance Metrics

Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.

MMLUGeneral knowledge & multi-task understanding
7570.0%vs8620.0%
Command R+
Llama 3.3 70B Instruct
HumanEvalPython coding & logic synthesis
7800.0%vs8800.0%
Command R+
Llama 3.3 70B Instruct
MATHComplex mathematical problem solving
6200.0%vs7500.0%
Command R+
Llama 3.3 70B Instruct
GPQAGraduate-level expert reasoning
4200.0%vs5200.0%
Command R+
Llama 3.3 70B Instruct
HellaSwagCommonsense reasoning and inference
8250.0%vs8850.0%
Command R+
Llama 3.3 70B Instruct
MT-BenchMulti-turn conversation flow quality
800.0%vs880.0%
Command R+
Llama 3.3 70B Instruct

Command R+ Quirks & Gotchas

  • โ–ธOptimized for RAG workflows โ€” best enterprise document search model
  • โ–ธTool calling requires explicit step definitions in Cohere's tool-use format

Llama 3.3 70B Instruct Quirks & Gotchas

  • โ–ธStable, well-documented self-hosted option with strong community support
  • โ–ธOutperformed by Llama 4 Maverick for agentic tool-calling workflows